Digitally-Assisted Mixed-Signal Wideband Compressive Sensing

Digitizing wideband signals requires very demanding analog-to-digital conversion (ADC) speed and resolution specifications. In this dissertation, a mixed-signal parallel compressive sensing system is proposed to realize the sensing of wideband sparse signals at sub-Nqyuist rate by exploiting the signal sparsity. The mixed-signal compressive sensing is realized with a parallel segmented compressive sensing (PSCS) front-end, which not only can filter out the harmonic spurs that leak from the local random generator, but also provides a tradeoff between the sampling rate and the system complexity such that a practical hardware implementation is possible. Moreover, the signal randomization in the system is able to spread the spurious energy due to ADC nonlinearity along the signal bandwidth rather than concentrate on a few frequencies as it is the case for a conventional ADC. This important new property relaxes the ADC SFDR requirement when sensing frequency-domain sparse signals. The mixed-signal compressive sensing system performance is greatly impacted by the accuracy of analog circuit components, especially with the scaling of CMOS technology. In this dissertation, the effect of the circuit imperfection in the mixed-signal compressive sensing system based on the PSCS front-end is investigated in detail, such as the finite settling time, the timing uncertainty and so on. An iterative background calibration algorithm based on LMS (Least Mean Square) is proposed, which is shown to be able to effectively calibrate the error due to the circuit nonideal factors. A low-speed prototype built with off-the-shelf components is presented. The prototype is able to sense sparse analog signals with up to 4 percent sparsity at 32 percent of the Nqyuist rate. Many practical constraints that arose during building the prototype such as circuit nonidealities are addressed in detail, which provides good insights for a future high-frequency integrated circuit implementation. Based on that, a high-frequency sub-Nyquist rate receiver exploiting the parallel compressive sensing is designed and fabricated with IBM90nm CMOS technology, and measurement results are presented to show the capability of wideband compressive sensing at sub-Nyquist rate. To the best of our knowledge, this prototype is the first reported integrated chip for wideband mixed-signal compressive sensing. The proposed prototype achieves 7 bits ENOB and 3 GS/s equivalent sampling rate in simulation assuming a 0.5 ps state-of-art jitter variance, whose FOM beats the FOM of the high speed state-of-the-art Nyquist ADCs by 2-3 times. The proposed mixed-signal compressive sensing system can be applied in various fields. In particular, its applications for wideband spectrum sensing for cognitive radios and spectrum analysis in RF tests are discussed in this work

Analog‐to‐Digital Conversion for Cognitive Radio: Subsampling, Interleaving, and Compressive Sensing

This chapter explores different analog-to-digital conversion techniques that are suitable to be implemented in cognitive radio receivers. This chapter details the fundamentals, advantages, and drawbacks of three promising techniques: subsampling, interleaving, and compressive sensing. Due to their major maturity, subsampling- and interleaving-based systems are described in further detail, whereas compressive sensing-based systems are described as a complement of the previous techniques for underutilized spectrum applications. The feasibility of these techniques as part of software-defined radio, multistandard, and spectrum sensing receivers is demonstrated by proposing different architectures with reduced complexity at circuit level, depending on the application requirements. Additionally, the chapter proposes different solutions to integrate the advantages of these techniques in a unique analog-to-digital conversion process

Frequency-domain characterization of random demodulation analog-to-information converters

The paper aims at proposing test methods for Analog-to-Information Converters (AICs).In particular, the objective of this work is to verify if figures of merit and test methods, currently defined in standards for traditional Analog-to-Digital Converters, can be applied to AICs based on the random demodulation architecture.For this purpose, an AIC prototype has been designed, starting from commercially available integrated circuits. A simulation analysis and an experimental investigation have been carried out to study the additional influencing factors such as the parameters of the reconstruction algorithm. Results show that standard figures of merit are in general capable of describing the performance of AICs, provided that they are slightly modified according to the proposals reported in the paper. In addition, test methods have to be modified in order to take into account the statistical behavior of AICs.</p

Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals

Wideband analog signals push contemporary analog-to-digital conversion systems to their performance limits. In many applications, however, sampling at the Nyquist rate is inefficient because the signals of interest contain only a small number of significant frequencies relative to the bandlimit, although the locations of the frequencies may not be known a priori. For this type of sparse signal, other sampling strategies are possible. This paper describes a new type of data acquisition system, called a random demodulator, that is constructed from robust, readily available components. Let K denote the total number of frequencies in the signal, and let W denote its bandlimit in Hz. Simulations suggest that the random demodulator requires just O(K log(W/K)) samples per second to stably reconstruct the signal. This sampling rate is exponentially lower than the Nyquist rate of W Hz. In contrast with Nyquist sampling, one must use nonlinear methods, such as convex programming, to recover the signal from the samples taken by the random demodulator. This paper provides a detailed theoretical analysis of the system's performance that supports the empirical observations.Comment: 24 pages, 8 figure

Beating Nyquist with Ultrafast Optical Pulses

Photonic sources readily provide several THz of analog bandwidth for information processing. Taking advantage of this fact, problems such as ultrawideband radio (RF) spectrum sensing, high performance radar, and analog-to-digital conversion can achieve significant performance gains with photonic techniques. Likewise, photonic imaging systems such as time-stretch microscopy have produced a breakthrough in continuous high speed imaging, enabling faster shutter speeds, higher frame rates, and greater gain-bandwidth product than is possible with continuous read-out CCDs and CMOS sensor arrays. However, imaging at this rate with traditional Nyquist sampling inevitably yields sustained data output on the order of 100 Gb/s or more, creating a significant challenge for storage and transmission. Real images and video are highly compressible, so this deluge of data is also highly inefficient. This thesis will address several techniques based on chirp-processing of ultrafast laser pulses that demonstrate real-time efficient compression of both electronic and optical signals, overcoming electronic bottlenecks via optical processing in the analog domain. Several systems will also be presented that permit greater information extraction from high throughput microscopy experiments by measuring quantitative phase images on a time-stretch microscope

Optical signal processing for efficient information networks

With the internet and rise of personal electronics there is an ever increasing amount of data collected and transmitted every day; modern communication systems will soon be overwhelmed. The driving force behind the demand is an increasing speed of signal acquisition, in the public domain, as well as medicine and industry; newer technologies allow massive amounts of data produced through text, voice, and video. This puts strain on both signal acquisition systems and communications systems to increase the total information flow. Transmission down fiber links is enabled by the large but limited bandwidth of optical fiber, and as we look toward the future, efficient use of the available optical bandwidth is paramount. I apply the large bandwidth of fiber and ultrafast speed of nonlinear optics to solve these problems, implementing high-speed and efficient signal acquisition and communication systems. With the increased volume of information being transferred, compression of data has become essential to allow multimedia communication. Data is acquired then compressed and transmitted, requiring massive computing power. Using the information theory technique coined “compressed sensing”, we demonstrate real time compression at signal acquisition, removing a timeconsuming and bandwidth inefficient step in a complete communication link. I use dispersion and nonlinear wave mixing in optical fiber, and gigahertz electro-optics to shape light at terahertz speeds, reaching towards the limit of compressed image acquisition. To complete a high-speed communications link, I investigate the use of Nyquist optical time division multiplexing to maximize spectral efficiency. The square spectral shape of a Nyquist pulse is ideal, but the pulse ripples on forever in the time domain, presenting problems for demultiplexing Nyquist signals at the receiver. I present a solution using coherent detection with a biorthogonal Nyquist pulse to eliminate interference from neighboring channels, and implement a proof of concept system using nonlinear wave mixing. Stable clock transfer is essential for coherent communication, but environmental fluctuations erode clock information, reducing the effective data rate of the communications channel. I present a versatile solution for stable time and frequency transfer using dispersion and nonlinear wave mixing in optical fiber

Sparsity-Aware Low-Power ADC Architecture with Advanced Reconstruction Algorithms

Compressive sensing (CS) technique enables a universal sub-Nyquist sampling of sparse and compressible signals, while still guaranteeing the reliable signal recovery. Its potential lies in the reduced analog-to-digital conversion rate in sampling broadband and/or multi-channel sparse signals, where conventional Nyquist-rate sampling are either technology impossible or extremely hardware costly. Nevertheless, there are many challenges in the CS hardware design. In coherent sampling, state-of-the-art mixed-signal CS front-ends, such as random demodulator and modulated wideband converter, suffer from high power and nonlinear hardware. In signal recovery, state-of-the-art CS reconstruction methods have tractable computational complexity and probabilistically guaranteed performance. However, they are still high cost (basis pursuit) or noise sensitive (matching pursuit). In this dissertation, we propose an asynchronous compressive sensing (ACS) front-end and advanced signal reconstruction algorithms to address these challenges. The ACS front-end consists of a continuous-time ternary encoding (CT-TE) scheme which converts signal amplitude variations into high-rate ternary timing signal, and a digital random sampler (DRS) which captures the ternary timing signal at sub-Nyquist rate. The CT-TE employs asynchronous sampling mechanism for pulsed-like input and has signal-dependent conversion rate. The DRS has low power, ease of massive integration, and excellent linearity in comparison to state-of-the-art mixed-signal CS front-ends. We propose two reconstruction algorithms. One is group-based total variation, which exploits piecewise-constant characteristics and achieves better mean squared error and faster convergence rate than the conventional TV scheme with moderate noise. The second algorithm is split-projection least squares (SPLS), which relies on a series of low-complexity and independent l2-norm problems with the prior on ternary-valued signal. The SPLS scheme has good noise robustness, low-cost signal reconstruction and facilitates a parallel hardware for real-time signal recovery. In application study, we propose multi-channel filter banks ACS front-end for the interference-robust radar. The proposed receiver performs reliable target detection with nearly 8-fold data compression than Nyquist-rate sampling in the presence of -50dBm wireless interference. We also propose an asynchronous compressed beamformer (ACB) for low-power portable diagnostic ultrasound. The proposed ACB achieves 9-fold data volume compression and only 4.4% contrast-to-noise ratio loss on the imaging results when compared with the Nyquist-rate ADCs